
where \(\eta \)  is the learning rate and \(\xi \)  is a mini-batch for the current update. Then, we restrict the values of \(w^k\)  to \([-1, 1]\)  as \(w^k \leftarrow \text{clip}(w^k, -1, 1),\) 
where \(\text{clip}(\cdot , -1, 1)\)  projects each input to 1 (-1) for any input larger (smaller) than 1 (-1), or returns the same value as the input. Otherwise, \(w^k\)  can become very large without a meaningful impact on quantization [1]}. After each training, \(w^k\)  are quantized as \(w^{Q, k}\) .
